Few-Shot Learning for Rooftop Detection in Satellite Imagery

Deep Learning Tutorial

Giorgio Coppala, Nadine Daum, Elena Dreyer, Nico Reichardt

Tentative Structure (delete me)

  • problem setting (policy relevance)

  • dataset geneva

  • model & methods

  • prototypical Networks

  • main notebook in detail

  • results to expect

  • wrap-up

Problem Setting

  • Cities need accurate rooftop maps to plan and scale solar PV installations

  • Manual rooftop labeling is slow and costly

  • Every city looks different → traditional models do not generalize well

Idea:

  • Few-shot learning makes segmentation possible with only a handful of labeled examples

Dataset: Geneva

  • Satellite Images: High-resolution RGB satellite images of Geneva
  • Size: 1,050 labeled image-mask pairs
  • Task: Binary segmentation masks (rooftop vs background)
  • Geographic splits: 3 grids/ neighborhoods (1301_11, 1301_13, 1301_31)
  • Image size: 250x250 pixels
  • Categories: Industrial, Residential

Model & Methods

  • Data Preprocessing

  • Model Architecture

  • Few-Shot in a Nutshell (modified figure from paper)

  • Few-Shot in implementation (ntoebook reference/ pseudocode for logic?)

  • Training strategy

  • Loss function

  • Evaluation metrics

Prototypical Networks

  • high-level schematic (support → prototype → similarity → segmentation)

  • literature reference: SRPNet

Main Notebook in Detail

how deep should we go?

lets discuss that regarding time

(presentation should be 10 minutes, followed by 5 minutes of Q&A)

Expected Results

  • Show performance for 1-shot / 5-shot / full-data comparison

  • Show predicted masks

Open to Discuss:

  • strengths

  • weaknesses

  • failure cases (shadows, tiny rooftops)

Wrap-Up: GitHub Repo

insert more from discussion + memo here

What we have so far:

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What we still need to finalize:

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Questions to discuss in class/ lynn

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